Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
# Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 608, number of events= 79
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.570e-01 6.998e-01 2.348e-01 -1.520 0.1285
Age 3.071e-02 1.031e+00 1.378e-02 2.229 0.0258 *
Gendermale 7.655e-01 2.150e+00 3.277e-01 2.336 0.0195 *
ORdate_year2003 -1.131e+00 3.228e-01 5.869e-01 -1.927 0.0540 .
ORdate_year2004 -7.715e-01 4.623e-01 5.189e-01 -1.487 0.1371
ORdate_year2005 -4.270e-01 6.524e-01 4.701e-01 -0.908 0.3637
ORdate_year2006 -3.803e-01 6.836e-01 4.692e-01 -0.811 0.4176
ORdate_year2007 -1.480e+00 2.275e-01 6.281e-01 -2.357 0.0184 *
ORdate_year2008 -2.539e-01 7.758e-01 4.763e-01 -0.533 0.5940
ORdate_year2009 -9.296e-01 3.947e-01 5.392e-01 -1.724 0.0847 .
ORdate_year2010 -5.937e-01 5.523e-01 5.869e-01 -1.012 0.3117
ORdate_year2011 -1.509e+00 2.211e-01 8.052e-01 -1.874 0.0609 .
ORdate_year2012 -8.457e-01 4.293e-01 6.949e-01 -1.217 0.2236
ORdate_year2013 -1.715e+01 3.569e-08 3.792e+03 -0.005 0.9964
ORdate_year2014 -1.681e+01 4.994e-08 4.843e+03 -0.003 0.9972
ORdate_year2015 -1.612e+01 9.973e-08 4.743e+03 -0.003 0.9973
ORdate_year2016 -1.630e+01 8.375e-08 8.557e+03 -0.002 0.9985
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.998e-01 1.429e+00 0.44164 1.1088
Age 1.031e+00 9.698e-01 1.00372 1.0594
Gendermale 2.150e+00 4.651e-01 1.13108 4.0874
ORdate_year2003 3.228e-01 3.098e+00 0.10219 1.0197
ORdate_year2004 4.623e-01 2.163e+00 0.16720 1.2783
ORdate_year2005 6.524e-01 1.533e+00 0.25967 1.6394
ORdate_year2006 6.836e-01 1.463e+00 0.27255 1.7147
ORdate_year2007 2.275e-01 4.395e+00 0.06643 0.7793
ORdate_year2008 7.758e-01 1.289e+00 0.30498 1.9732
ORdate_year2009 3.947e-01 2.533e+00 0.13720 1.1357
ORdate_year2010 5.523e-01 1.811e+00 0.17480 1.7448
ORdate_year2011 2.211e-01 4.524e+00 0.04561 1.0713
ORdate_year2012 4.293e-01 2.330e+00 0.10997 1.6757
ORdate_year2013 3.569e-08 2.802e+07 0.00000 Inf
ORdate_year2014 4.994e-08 2.002e+07 0.00000 Inf
ORdate_year2015 9.973e-08 1.003e+07 0.00000 Inf
ORdate_year2016 8.375e-08 1.194e+07 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.659 (se = 0.029 )
Likelihood ratio test= 29.34 on 17 df, p=0.03
Wald test = 23.89 on 17 df, p=0.1
Score (logrank) test = 26.91 on 17 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epmajor.3years
Protein...................: HDAC9
Effect size...............: -0.35699
Standard error............: 0.234834
Odds ratio (effect size)..: 0.7
Lower 95% CI..............: 0.442
Upper 95% CI..............: 1.109
T-value...................: -1.520178
P-value...................: 0.1284662
Sample size in model......: 608
Number of events..........: 79
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 608, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.767e-01 6.862e-01 3.043e-01 -1.238 0.2157
Age 2.715e-02 1.028e+00 1.774e-02 1.530 0.1259
Gendermale 3.733e-01 1.453e+00 3.746e-01 0.997 0.3190
ORdate_year2003 -9.921e-01 3.708e-01 7.657e-01 -1.296 0.1951
ORdate_year2004 -8.059e-01 4.467e-01 7.090e-01 -1.137 0.2557
ORdate_year2005 -3.037e-01 7.381e-01 6.135e-01 -0.495 0.6206
ORdate_year2006 -7.952e-03 9.921e-01 5.921e-01 -0.013 0.9893
ORdate_year2007 -1.522e+00 2.182e-01 8.675e-01 -1.755 0.0793 .
ORdate_year2008 -1.811e-01 8.344e-01 6.274e-01 -0.289 0.7729
ORdate_year2009 -1.517e+00 2.194e-01 8.714e-01 -1.741 0.0817 .
ORdate_year2010 -1.578e-01 8.540e-01 7.092e-01 -0.222 0.8239
ORdate_year2011 -1.554e+00 2.113e-01 1.122e+00 -1.385 0.1660
ORdate_year2012 -5.545e-01 5.744e-01 8.714e-01 -0.636 0.5246
ORdate_year2013 -1.699e+01 4.164e-08 5.061e+03 -0.003 0.9973
ORdate_year2014 -1.666e+01 5.831e-08 6.455e+03 -0.003 0.9979
ORdate_year2015 -1.622e+01 9.003e-08 6.501e+03 -0.002 0.9980
ORdate_year2016 -1.654e+01 6.526e-08 1.140e+04 -0.001 0.9988
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.862e-01 1.457e+00 0.37795 1.246
Age 1.028e+00 9.732e-01 0.99241 1.064
Gendermale 1.453e+00 6.884e-01 0.69706 3.027
ORdate_year2003 3.708e-01 2.697e+00 0.08268 1.663
ORdate_year2004 4.467e-01 2.239e+00 0.11131 1.793
ORdate_year2005 7.381e-01 1.355e+00 0.22175 2.457
ORdate_year2006 9.921e-01 1.008e+00 0.31087 3.166
ORdate_year2007 2.182e-01 4.582e+00 0.03985 1.195
ORdate_year2008 8.344e-01 1.198e+00 0.24397 2.854
ORdate_year2009 2.194e-01 4.558e+00 0.03976 1.210
ORdate_year2010 8.540e-01 1.171e+00 0.21271 3.429
ORdate_year2011 2.113e-01 4.732e+00 0.02344 1.906
ORdate_year2012 5.744e-01 1.741e+00 0.10409 3.169
ORdate_year2013 4.164e-08 2.402e+07 0.00000 Inf
ORdate_year2014 5.831e-08 1.715e+07 0.00000 Inf
ORdate_year2015 9.003e-08 1.111e+07 0.00000 Inf
ORdate_year2016 6.526e-08 1.532e+07 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.678 (se = 0.035 )
Likelihood ratio test= 19.58 on 17 df, p=0.3
Wald test = 15.36 on 17 df, p=0.6
Score (logrank) test = 18.04 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epstroke.3years
Protein...................: HDAC9
Effect size...............: -0.376659
Standard error............: 0.304261
Odds ratio (effect size)..: 0.686
Lower 95% CI..............: 0.378
Upper 95% CI..............: 1.246
T-value...................: -1.237946
P-value...................: 0.215736
Sample size in model......: 608
Number of events..........: 47
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 608, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.436e-01 7.092e-01 3.018e-01 -1.138 0.2549
Age 5.263e-03 1.005e+00 1.746e-02 0.301 0.7631
Gendermale 1.087e+00 2.966e+00 4.758e-01 2.285 0.0223 *
ORdate_year2003 -6.180e-01 5.390e-01 6.069e-01 -1.018 0.3086
ORdate_year2004 -1.436e+00 2.379e-01 7.325e-01 -1.960 0.0500 *
ORdate_year2005 -6.738e-01 5.097e-01 5.879e-01 -1.146 0.2517
ORdate_year2006 -3.341e-01 7.160e-01 5.482e-01 -0.609 0.5423
ORdate_year2007 -1.082e+00 3.389e-01 6.726e-01 -1.609 0.1076
ORdate_year2008 -7.672e-01 4.643e-01 6.336e-01 -1.211 0.2260
ORdate_year2009 -1.078e+00 3.401e-01 6.741e-01 -1.600 0.1096
ORdate_year2010 -1.868e+00 1.545e-01 1.096e+00 -1.704 0.0884 .
ORdate_year2011 -1.777e+00 1.691e-01 1.098e+00 -1.619 0.1055
ORdate_year2012 -1.586e+00 2.047e-01 1.100e+00 -1.442 0.1494
ORdate_year2013 -1.739e+01 2.809e-08 5.091e+03 -0.003 0.9973
ORdate_year2014 -1.714e+01 3.587e-08 6.484e+03 -0.003 0.9979
ORdate_year2015 -1.643e+01 7.331e-08 6.318e+03 -0.003 0.9979
ORdate_year2016 -1.629e+01 8.444e-08 1.132e+04 -0.001 0.9989
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.092e-01 1.410e+00 0.39254 1.2814
Age 1.005e+00 9.948e-01 0.97146 1.0403
Gendermale 2.966e+00 3.371e-01 1.16735 7.5375
ORdate_year2003 5.390e-01 1.855e+00 0.16405 1.7710
ORdate_year2004 2.379e-01 4.204e+00 0.05661 0.9997
ORdate_year2005 5.097e-01 1.962e+00 0.16104 1.6135
ORdate_year2006 7.160e-01 1.397e+00 0.24450 2.0967
ORdate_year2007 3.389e-01 2.951e+00 0.09069 1.2663
ORdate_year2008 4.643e-01 2.154e+00 0.13413 1.6075
ORdate_year2009 3.401e-01 2.940e+00 0.09076 1.2748
ORdate_year2010 1.545e-01 6.474e+00 0.01802 1.3241
ORdate_year2011 1.691e-01 5.914e+00 0.01966 1.4544
ORdate_year2012 2.047e-01 4.884e+00 0.02371 1.7684
ORdate_year2013 2.809e-08 3.560e+07 0.00000 Inf
ORdate_year2014 3.587e-08 2.787e+07 0.00000 Inf
ORdate_year2015 7.331e-08 1.364e+07 0.00000 Inf
ORdate_year2016 8.444e-08 1.184e+07 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.668 (se = 0.037 )
Likelihood ratio test= 19.7 on 17 df, p=0.3
Wald test = 15.53 on 17 df, p=0.6
Score (logrank) test = 18.03 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcoronary.3years
Protein...................: HDAC9
Effect size...............: -0.343584
Standard error............: 0.301808
Odds ratio (effect size)..: 0.709
Lower 95% CI..............: 0.393
Upper 95% CI..............: 1.281
T-value...................: -1.138421
P-value...................: 0.2549448
Sample size in model......: 608
Number of events..........: 47
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 12,13,14,15,16,17 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 11,12,13,14,15,16 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 608, number of events= 27
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -8.607e-01 4.229e-01 4.346e-01 -1.980 0.04768 *
Age 7.113e-02 1.074e+00 2.628e-02 2.707 0.00679 **
Gendermale 6.115e-01 1.843e+00 5.456e-01 1.121 0.26237
ORdate_year2003 -1.596e+00 2.027e-01 8.395e-01 -1.901 0.05732 .
ORdate_year2004 -1.704e+00 1.819e-01 8.376e-01 -2.035 0.04187 *
ORdate_year2005 -9.918e-01 3.709e-01 6.332e-01 -1.566 0.11730
ORdate_year2006 -1.827e+00 1.609e-01 8.390e-01 -2.178 0.02942 *
ORdate_year2007 -1.713e+00 1.804e-01 8.386e-01 -2.042 0.04111 *
ORdate_year2008 -1.245e+00 2.878e-01 7.311e-01 -1.703 0.08850 .
ORdate_year2009 -9.372e-01 3.917e-01 6.856e-01 -1.367 0.17163
ORdate_year2010 -1.059e+00 3.469e-01 8.396e-01 -1.261 0.20731
ORdate_year2011 -1.917e+01 4.712e-09 5.489e+03 -0.003 0.99721
ORdate_year2012 -1.930e+01 4.137e-09 6.718e+03 -0.003 0.99771
ORdate_year2013 -1.960e+01 3.087e-09 1.480e+04 -0.001 0.99894
ORdate_year2014 -1.877e+01 7.060e-09 1.853e+04 -0.001 0.99919
ORdate_year2015 -1.790e+01 1.677e-08 1.934e+04 -0.001 0.99926
ORdate_year2016 -1.875e+01 7.211e-09 3.554e+04 -0.001 0.99958
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 4.229e-01 2.365e+00 0.18041 0.9913
Age 1.074e+00 9.313e-01 1.01982 1.1305
Gendermale 1.843e+00 5.425e-01 0.63265 5.3700
ORdate_year2003 2.027e-01 4.932e+00 0.03912 1.0509
ORdate_year2004 1.819e-01 5.497e+00 0.03523 0.9393
ORdate_year2005 3.709e-01 2.696e+00 0.10722 1.2832
ORdate_year2006 1.609e-01 6.216e+00 0.03107 0.8329
ORdate_year2007 1.804e-01 5.544e+00 0.03486 0.9332
ORdate_year2008 2.878e-01 3.474e+00 0.06867 1.2063
ORdate_year2009 3.917e-01 2.553e+00 0.10219 1.5016
ORdate_year2010 3.469e-01 2.883e+00 0.06691 1.7984
ORdate_year2011 4.712e-09 2.122e+08 0.00000 Inf
ORdate_year2012 4.137e-09 2.417e+08 0.00000 Inf
ORdate_year2013 3.087e-09 3.239e+08 0.00000 Inf
ORdate_year2014 7.060e-09 1.417e+08 0.00000 Inf
ORdate_year2015 1.677e-08 5.964e+07 0.00000 Inf
ORdate_year2016 7.211e-09 1.387e+08 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.792 (se = 0.037 )
Likelihood ratio test= 29.29 on 17 df, p=0.03
Wald test = 15.29 on 17 df, p=0.6
Score (logrank) test = 29.48 on 17 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcvdeath.3years
Protein...................: HDAC9
Effect size...............: -0.860664
Standard error............: 0.434641
Odds ratio (effect size)..: 0.423
Lower 95% CI..............: 0.18
Upper 95% CI..............: 0.991
T-value...................: -1.980172
P-value...................: 0.04768416
Sample size in model......: 608
Number of events..........: 27

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.hdac9.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
# Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17,33,34,35 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16,32,33,34 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 530, number of events= 67
(81 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.318e-01 8.765e-01 2.604e-01 -0.506 0.61280
Age 4.473e-02 1.046e+00 1.822e-02 2.454 0.01411 *
Gendermale 7.081e-01 2.030e+00 3.675e-01 1.927 0.05401 .
ORdate_year2003 -1.039e+00 3.540e-01 6.559e-01 -1.583 0.11334
ORdate_year2004 -5.447e-01 5.800e-01 5.701e-01 -0.955 0.33937
ORdate_year2005 -6.583e-02 9.363e-01 5.329e-01 -0.124 0.90168
ORdate_year2006 6.261e-03 1.006e+00 5.220e-01 0.012 0.99043
ORdate_year2007 -1.106e+00 3.309e-01 6.744e-01 -1.640 0.10107
ORdate_year2008 7.153e-02 1.074e+00 5.518e-01 0.130 0.89686
ORdate_year2009 -9.449e-01 3.887e-01 6.423e-01 -1.471 0.14124
ORdate_year2010 -2.599e-01 7.711e-01 6.334e-01 -0.410 0.68158
ORdate_year2011 -1.020e+00 3.605e-01 8.449e-01 -1.208 0.22715
ORdate_year2012 -2.814e-01 7.548e-01 8.582e-01 -0.328 0.74302
ORdate_year2013 -1.658e+01 6.296e-08 4.559e+03 -0.004 0.99710
ORdate_year2014 -1.612e+01 9.973e-08 1.005e+04 -0.002 0.99872
ORdate_year2015 -1.616e+01 9.562e-08 4.869e+03 -0.003 0.99735
ORdate_year2016 -1.527e+01 2.330e-07 1.005e+04 -0.002 0.99879
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeyes 7.024e-01 2.019e+00 5.423e-01 1.295 0.19529
DiabetesStatusDiabetes 7.616e-01 2.142e+00 2.689e-01 2.832 0.00463 **
SmokerStatusEx-smoker -5.310e-01 5.880e-01 2.720e-01 -1.952 0.05093 .
SmokerStatusNever smoked -8.937e-01 4.091e-01 4.383e-01 -2.039 0.04143 *
Med.Statin.LLDyes -2.653e-01 7.670e-01 2.906e-01 -0.913 0.36141
Med.all.antiplateletyes -2.405e-01 7.863e-01 4.075e-01 -0.590 0.55513
GFR_MDRD -1.692e-04 9.998e-01 1.982e-04 -0.853 0.39341
BMI 7.750e-04 1.001e+00 4.414e-04 1.756 0.07916 .
MedHx_CVDNo -6.775e-01 5.079e-01 3.037e-01 -2.231 0.02569 *
stenose50-70% 1.549e+01 5.346e+06 5.017e+03 0.003 0.99754
stenose70-90% 1.611e+01 9.964e+06 5.017e+03 0.003 0.99744
stenose90-99% 1.618e+01 1.060e+07 5.017e+03 0.003 0.99743
stenose100% (Occlusion) 5.304e-01 1.700e+00 6.605e+03 0.000 0.99994
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -6.599e-01 5.169e-01 1.123e+04 0.000 0.99995
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.765e-01 1.141e+00 0.52619 1.4602
Age 1.046e+00 9.563e-01 1.00905 1.0838
Gendermale 2.030e+00 4.926e-01 0.98786 4.1723
ORdate_year2003 3.540e-01 2.825e+00 0.09786 1.2802
ORdate_year2004 5.800e-01 1.724e+00 0.18974 1.7730
ORdate_year2005 9.363e-01 1.068e+00 0.32949 2.6606
ORdate_year2006 1.006e+00 9.938e-01 0.36174 2.7992
ORdate_year2007 3.309e-01 3.022e+00 0.08823 1.2411
ORdate_year2008 1.074e+00 9.310e-01 0.36421 3.1679
ORdate_year2009 3.887e-01 2.573e+00 0.11039 1.3688
ORdate_year2010 7.711e-01 1.297e+00 0.22282 2.6687
ORdate_year2011 3.605e-01 2.774e+00 0.06881 1.8880
ORdate_year2012 7.548e-01 1.325e+00 0.14038 4.0579
ORdate_year2013 6.296e-08 1.588e+07 0.00000 Inf
ORdate_year2014 9.973e-08 1.003e+07 0.00000 Inf
ORdate_year2015 9.562e-08 1.046e+07 0.00000 Inf
ORdate_year2016 2.329e-07 4.293e+06 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeyes 2.019e+00 4.954e-01 0.69727 5.8437
DiabetesStatusDiabetes 2.142e+00 4.669e-01 1.26426 3.6279
SmokerStatusEx-smoker 5.880e-01 1.701e+00 0.34503 1.0022
SmokerStatusNever smoked 4.091e-01 2.444e+00 0.17331 0.9659
Med.Statin.LLDyes 7.670e-01 1.304e+00 0.43391 1.3558
Med.all.antiplateletyes 7.863e-01 1.272e+00 0.35377 1.7475
GFR_MDRD 9.998e-01 1.000e+00 0.99944 1.0002
BMI 1.001e+00 9.992e-01 0.99991 1.0016
MedHx_CVDNo 5.079e-01 1.969e+00 0.28006 0.9210
stenose50-70% 5.346e+06 1.871e-07 0.00000 Inf
stenose70-90% 9.964e+06 1.004e-07 0.00000 Inf
stenose90-99% 1.060e+07 9.435e-08 0.00000 Inf
stenose100% (Occlusion) 1.700e+00 5.883e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 5.169e-01 1.935e+00 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.763 (se = 0.026 )
Likelihood ratio test= 59 on 31 df, p=0.002
Wald test = 44.52 on 31 df, p=0.05
Score (logrank) test = 57.21 on 31 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epmajor.3years
Protein...................: HDAC9
Effect size...............: -0.131769
Standard error............: 0.260373
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.526
Upper 95% CI..............: 1.46
T-value...................: -0.506077
P-value...................: 0.6128024
Sample size in model......: 530
Number of events..........: 67
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17,33,34,35 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16,32,33,34 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 530, number of events= 37
(81 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.703e-01 8.434e-01 3.510e-01 -0.485 0.6276
Age 5.147e-02 1.053e+00 2.370e-02 2.172 0.0298 *
Gendermale 2.527e-01 1.287e+00 4.348e-01 0.581 0.5612
ORdate_year2003 -1.044e+00 3.522e-01 9.289e-01 -1.124 0.2612
ORdate_year2004 -6.143e-01 5.410e-01 8.312e-01 -0.739 0.4599
ORdate_year2005 -1.172e-01 8.894e-01 7.372e-01 -0.159 0.8736
ORdate_year2006 4.584e-01 1.582e+00 6.917e-01 0.663 0.5075
ORdate_year2007 -1.053e+00 3.487e-01 9.485e-01 -1.111 0.2667
ORdate_year2008 7.231e-02 1.075e+00 7.590e-01 0.095 0.9241
ORdate_year2009 -1.800e+00 1.654e-01 1.192e+00 -1.510 0.1311
ORdate_year2010 1.386e-01 1.149e+00 8.076e-01 0.172 0.8638
ORdate_year2011 -9.355e-01 3.924e-01 1.181e+00 -0.792 0.4283
ORdate_year2012 -1.095e-01 8.963e-01 1.209e+00 -0.091 0.9278
ORdate_year2013 -1.640e+01 7.545e-08 6.617e+03 -0.002 0.9980
ORdate_year2014 -1.597e+01 1.158e-07 1.419e+04 -0.001 0.9991
ORdate_year2015 -1.627e+01 8.581e-08 7.550e+03 -0.002 0.9983
ORdate_year2016 -1.526e+01 2.347e-07 1.419e+04 -0.001 0.9991
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeyes 3.872e-01 1.473e+00 6.468e-01 0.599 0.5494
DiabetesStatusDiabetes 5.080e-01 1.662e+00 3.825e-01 1.328 0.1842
SmokerStatusEx-smoker -5.848e-01 5.572e-01 3.580e-01 -1.634 0.1023
SmokerStatusNever smoked -1.694e+00 1.837e-01 7.594e-01 -2.231 0.0257 *
Med.Statin.LLDyes -2.400e-01 7.866e-01 3.863e-01 -0.621 0.5344
Med.all.antiplateletyes -1.004e-01 9.045e-01 5.734e-01 -0.175 0.8610
GFR_MDRD 2.910e-04 1.000e+00 2.682e-04 1.085 0.2779
BMI 1.079e-03 1.001e+00 5.996e-04 1.799 0.0721 .
MedHx_CVDNo -5.917e-01 5.534e-01 3.977e-01 -1.488 0.1368
stenose50-70% 1.563e+01 6.126e+06 6.652e+03 0.002 0.9981
stenose70-90% 1.568e+01 6.437e+06 6.652e+03 0.002 0.9981
stenose90-99% 1.574e+01 6.875e+06 6.652e+03 0.002 0.9981
stenose100% (Occlusion) -1.606e-01 8.516e-01 8.497e+03 0.000 1.0000
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.751e+00 1.737e-01 1.567e+04 0.000 0.9999
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.434e-01 1.186e+00 0.42391 1.6782
Age 1.053e+00 9.498e-01 1.00505 1.1029
Gendermale 1.287e+00 7.767e-01 0.54902 3.0190
ORdate_year2003 3.522e-01 2.840e+00 0.05703 2.1747
ORdate_year2004 5.410e-01 1.848e+00 0.10609 2.7591
ORdate_year2005 8.894e-01 1.124e+00 0.20968 3.7722
ORdate_year2006 1.582e+00 6.323e-01 0.40766 6.1356
ORdate_year2007 3.487e-01 2.868e+00 0.05434 2.2380
ORdate_year2008 1.075e+00 9.302e-01 0.24287 4.7580
ORdate_year2009 1.654e-01 6.047e+00 0.01599 1.7100
ORdate_year2010 1.149e+00 8.706e-01 0.23590 5.5927
ORdate_year2011 3.924e-01 2.548e+00 0.03876 3.9720
ORdate_year2012 8.963e-01 1.116e+00 0.08386 9.5788
ORdate_year2013 7.545e-08 1.325e+07 0.00000 Inf
ORdate_year2014 1.158e-07 8.636e+06 0.00000 Inf
ORdate_year2015 8.581e-08 1.165e+07 0.00000 Inf
ORdate_year2016 2.347e-07 4.261e+06 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeyes 1.473e+00 6.789e-01 0.41460 5.2326
DiabetesStatusDiabetes 1.662e+00 6.017e-01 0.78520 3.5174
SmokerStatusEx-smoker 5.572e-01 1.795e+00 0.27625 1.1239
SmokerStatusNever smoked 1.837e-01 5.443e+00 0.04147 0.8139
Med.Statin.LLDyes 7.866e-01 1.271e+00 0.36892 1.6772
Med.all.antiplateletyes 9.045e-01 1.106e+00 0.29399 2.7825
GFR_MDRD 1.000e+00 9.997e-01 0.99977 1.0008
BMI 1.001e+00 9.989e-01 0.99990 1.0023
MedHx_CVDNo 5.534e-01 1.807e+00 0.25382 1.2066
stenose50-70% 6.126e+06 1.633e-07 0.00000 Inf
stenose70-90% 6.437e+06 1.553e-07 0.00000 Inf
stenose90-99% 6.875e+06 1.455e-07 0.00000 Inf
stenose100% (Occlusion) 8.516e-01 1.174e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 1.737e-01 5.759e+00 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.78 (se = 0.032 )
Likelihood ratio test= 37.58 on 31 df, p=0.2
Wald test = 26.88 on 31 df, p=0.7
Score (logrank) test = 36.13 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epstroke.3years
Protein...................: HDAC9
Effect size...............: -0.170258
Standard error............: 0.35101
Odds ratio (effect size)..: 0.843
Lower 95% CI..............: 0.424
Upper 95% CI..............: 1.678
T-value...................: -0.485054
P-value...................: 0.6276384
Sample size in model......: 530
Number of events..........: 37
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 14,15,16,17,34,35 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 13,14,15,16,33,34 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 530, number of events= 42
(81 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.917e-01 8.256e-01 3.275e-01 -0.585 0.55831
Age 4.240e-03 1.004e+00 2.322e-02 0.183 0.85513
Gendermale 1.516e+00 4.554e+00 6.104e-01 2.483 0.01301 *
ORdate_year2003 -3.999e-01 6.704e-01 6.693e-01 -0.597 0.55018
ORdate_year2004 -1.227e+00 2.933e-01 7.828e-01 -1.567 0.11711
ORdate_year2005 -1.917e-01 8.256e-01 6.635e-01 -0.289 0.77266
ORdate_year2006 -3.560e-01 7.005e-01 6.501e-01 -0.548 0.58394
ORdate_year2007 -8.830e-01 4.135e-01 8.111e-01 -1.089 0.27628
ORdate_year2008 -4.915e-01 6.117e-01 7.423e-01 -0.662 0.50790
ORdate_year2009 -8.584e-01 4.238e-01 7.545e-01 -1.138 0.25525
ORdate_year2010 -1.852e+00 1.569e-01 1.148e+00 -1.613 0.10664
ORdate_year2011 -1.669e+00 1.885e-01 1.162e+00 -1.436 0.15109
ORdate_year2012 -9.168e-01 3.998e-01 1.167e+00 -0.786 0.43213
ORdate_year2013 -1.926e+01 4.303e-09 1.620e+04 -0.001 0.99905
ORdate_year2014 -1.794e+01 1.620e-08 3.501e+04 -0.001 0.99959
ORdate_year2015 -1.742e+01 2.726e-08 1.583e+04 -0.001 0.99912
ORdate_year2016 -1.742e+01 2.719e-08 3.501e+04 0.000 0.99960
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeyes 1.573e+00 4.821e+00 1.031e+00 1.526 0.12702
DiabetesStatusDiabetes 3.327e-01 1.395e+00 3.492e-01 0.953 0.34068
SmokerStatusEx-smoker -2.464e-01 7.816e-01 3.558e-01 -0.693 0.48857
SmokerStatusNever smoked 1.276e-01 1.136e+00 4.770e-01 0.267 0.78908
Med.Statin.LLDyes 4.294e-01 1.536e+00 4.406e-01 0.974 0.32983
Med.all.antiplateletyes 2.228e-02 1.023e+00 5.714e-01 0.039 0.96890
GFR_MDRD -4.257e-04 9.996e-01 2.562e-04 -1.662 0.09661 .
BMI 4.625e-04 1.000e+00 5.521e-04 0.838 0.40220
MedHx_CVDNo -1.129e+00 3.234e-01 4.271e-01 -2.643 0.00822 **
stenose50-70% 1.097e+00 2.994e+00 1.877e+04 0.000 0.99995
stenose70-90% 1.896e+01 1.717e+08 1.797e+04 0.001 0.99916
stenose90-99% 1.884e+01 1.519e+08 1.797e+04 0.001 0.99916
stenose100% (Occlusion) 1.314e+00 3.722e+00 2.298e+04 0.000 0.99995
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.578e+00 4.847e+00 3.935e+04 0.000 0.99997
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.256e-01 1.211e+00 0.43451 1.569
Age 1.004e+00 9.958e-01 0.95957 1.051
Gendermale 4.554e+00 2.196e-01 1.37644 15.064
ORdate_year2003 6.704e-01 1.492e+00 0.18057 2.489
ORdate_year2004 2.933e-01 3.410e+00 0.06324 1.360
ORdate_year2005 8.256e-01 1.211e+00 0.22491 3.030
ORdate_year2006 7.005e-01 1.428e+00 0.19591 2.505
ORdate_year2007 4.135e-01 2.418e+00 0.08435 2.027
ORdate_year2008 6.117e-01 1.635e+00 0.14279 2.621
ORdate_year2009 4.238e-01 2.359e+00 0.09659 1.860
ORdate_year2010 1.569e-01 6.373e+00 0.01654 1.488
ORdate_year2011 1.885e-01 5.306e+00 0.01931 1.839
ORdate_year2012 3.998e-01 2.501e+00 0.04060 3.938
ORdate_year2013 4.303e-09 2.324e+08 0.00000 Inf
ORdate_year2014 1.620e-08 6.172e+07 0.00000 Inf
ORdate_year2015 2.726e-08 3.668e+07 0.00000 Inf
ORdate_year2016 2.719e-08 3.678e+07 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeyes 4.821e+00 2.074e-01 0.63929 36.356
DiabetesStatusDiabetes 1.395e+00 7.170e-01 0.70348 2.765
SmokerStatusEx-smoker 7.816e-01 1.279e+00 0.38919 1.570
SmokerStatusNever smoked 1.136e+00 8.802e-01 0.44605 2.894
Med.Statin.LLDyes 1.536e+00 6.509e-01 0.64775 3.644
Med.all.antiplateletyes 1.023e+00 9.780e-01 0.33366 3.134
GFR_MDRD 9.996e-01 1.000e+00 0.99907 1.000
BMI 1.000e+00 9.995e-01 0.99938 1.002
MedHx_CVDNo 3.234e-01 3.092e+00 0.14003 0.747
stenose50-70% 2.994e+00 3.340e-01 0.00000 Inf
stenose70-90% 1.717e+08 5.826e-09 0.00000 Inf
stenose90-99% 1.519e+08 6.583e-09 0.00000 Inf
stenose100% (Occlusion) 3.722e+00 2.687e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 4.847e+00 2.063e-01 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.784 (se = 0.026 )
Likelihood ratio test= 46.96 on 31 df, p=0.03
Wald test = 24.64 on 31 df, p=0.8
Score (logrank) test = 38.53 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcoronary.3years
Protein...................: HDAC9
Effect size...............: -0.191689
Standard error............: 0.327479
Odds ratio (effect size)..: 0.826
Lower 95% CI..............: 0.435
Upper 95% CI..............: 1.569
T-value...................: -0.585348
P-value...................: 0.5583135
Sample size in model......: 530
Number of events..........: 42
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
314 297
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 12,13,14,15,16,17,24,33,34,35,38 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 11,12,13,14,15,16,23,32,33,34,37 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 530, number of events= 23
(81 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -4.178e-01 6.585e-01 4.762e-01 -0.877 0.3803
Age 7.242e-02 1.075e+00 3.502e-02 2.068 0.0387 *
Gendermale 3.605e-01 1.434e+00 5.808e-01 0.621 0.5348
ORdate_year2003 -1.450e+00 2.346e-01 9.303e-01 -1.559 0.1191
ORdate_year2004 -1.161e+00 3.132e-01 9.034e-01 -1.285 0.1987
ORdate_year2005 -4.916e-01 6.117e-01 7.785e-01 -0.631 0.5278
ORdate_year2006 -1.264e+00 2.825e-01 9.076e-01 -1.393 0.1637
ORdate_year2007 -1.552e+00 2.118e-01 9.501e-01 -1.634 0.1023
ORdate_year2008 -5.706e-01 5.652e-01 8.332e-01 -0.685 0.4935
ORdate_year2009 -1.415e+00 2.430e-01 9.732e-01 -1.454 0.1460
ORdate_year2010 -5.791e-01 5.604e-01 9.477e-01 -0.611 0.5412
ORdate_year2011 -2.076e+01 9.612e-10 1.356e+04 -0.002 0.9988
ORdate_year2012 -2.036e+01 1.438e-09 2.044e+04 -0.001 0.9992
ORdate_year2013 -2.053e+01 1.209e-09 4.964e+04 0.000 0.9997
ORdate_year2014 -1.974e+01 2.686e-09 1.173e+05 0.000 0.9999
ORdate_year2015 -2.054e+01 1.202e-09 4.934e+04 0.000 0.9997
ORdate_year2016 -1.913e+01 4.896e-09 1.173e+05 0.000 0.9999
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeyes 1.907e+01 1.918e+08 7.276e+03 0.003 0.9979
DiabetesStatusDiabetes 1.212e+00 3.361e+00 4.799e-01 2.526 0.0115 *
SmokerStatusEx-smoker -8.080e-01 4.457e-01 4.866e-01 -1.661 0.0968 .
SmokerStatusNever smoked -1.105e+00 3.313e-01 8.126e-01 -1.360 0.1740
Med.Statin.LLDyes -5.452e-01 5.797e-01 4.731e-01 -1.152 0.2492
Med.all.antiplateletyes -1.117e+00 3.272e-01 5.923e-01 -1.886 0.0593 .
GFR_MDRD -6.217e-04 9.994e-01 3.621e-04 -1.717 0.0860 .
BMI 8.061e-04 1.001e+00 8.100e-04 0.995 0.3197
MedHx_CVDNo -7.928e-01 4.526e-01 5.899e-01 -1.344 0.1790
stenose50-70% 1.913e+01 2.025e+08 3.916e+04 0.000 0.9996
stenose70-90% 1.905e+01 1.880e+08 3.916e+04 0.000 0.9996
stenose90-99% 1.914e+01 2.049e+08 3.916e+04 0.000 0.9996
stenose100% (Occlusion) 2.515e+00 1.237e+01 4.692e+04 0.000 1.0000
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 3.679e+01 9.479e+15 1.256e+05 0.000 0.9998
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.585e-01 1.519e+00 0.25897 1.674
Age 1.075e+00 9.301e-01 1.00379 1.151
Gendermale 1.434e+00 6.973e-01 0.45939 4.477
ORdate_year2003 2.346e-01 4.263e+00 0.03788 1.453
ORdate_year2004 3.132e-01 3.193e+00 0.05331 1.840
ORdate_year2005 6.117e-01 1.635e+00 0.13299 2.813
ORdate_year2006 2.825e-01 3.539e+00 0.04770 1.674
ORdate_year2007 2.118e-01 4.721e+00 0.03290 1.363
ORdate_year2008 5.652e-01 1.769e+00 0.11040 2.893
ORdate_year2009 2.430e-01 4.116e+00 0.03607 1.637
ORdate_year2010 5.604e-01 1.784e+00 0.08746 3.591
ORdate_year2011 9.612e-10 1.040e+09 0.00000 Inf
ORdate_year2012 1.438e-09 6.954e+08 0.00000 Inf
ORdate_year2013 1.209e-09 8.269e+08 0.00000 Inf
ORdate_year2014 2.686e-09 3.723e+08 0.00000 Inf
ORdate_year2015 1.202e-09 8.323e+08 0.00000 Inf
ORdate_year2016 4.896e-09 2.043e+08 0.00000 Inf
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeyes 1.918e+08 5.214e-09 0.00000 Inf
DiabetesStatusDiabetes 3.361e+00 2.975e-01 1.31207 8.609
SmokerStatusEx-smoker 4.457e-01 2.243e+00 0.17174 1.157
SmokerStatusNever smoked 3.313e-01 3.018e+00 0.06738 1.629
Med.Statin.LLDyes 5.797e-01 1.725e+00 0.22937 1.465
Med.all.antiplateletyes 3.272e-01 3.056e+00 0.10249 1.045
GFR_MDRD 9.994e-01 1.001e+00 0.99867 1.000
BMI 1.001e+00 9.992e-01 0.99922 1.002
MedHx_CVDNo 4.526e-01 2.209e+00 0.14242 1.438
stenose50-70% 2.025e+08 4.938e-09 0.00000 Inf
stenose70-90% 1.880e+08 5.319e-09 0.00000 Inf
stenose90-99% 2.049e+08 4.880e-09 0.00000 Inf
stenose100% (Occlusion) 1.237e+01 8.084e-02 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 9.479e+15 1.055e-16 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.862 (se = 0.031 )
Likelihood ratio test= 46.37 on 31 df, p=0.04
Wald test = 23.26 on 31 df, p=0.8
Score (logrank) test = 42.37 on 31 df, p=0.08
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcvdeath.3years
Protein...................: HDAC9
Effect size...............: -0.417773
Standard error............: 0.476158
Odds ratio (effect size)..: 0.659
Lower 95% CI..............: 0.259
Upper 95% CI..............: 1.674
T-value...................: -0.877382
P-value...................: 0.3802791
Sample size in model......: 530
Number of events..........: 23

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.hdac9.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)